pipeline_4_animalcules
wentao
2021/5/14
准备数据
library(animalcules)
library(SummarizedExperiment)
library(MultiAssayExperiment)
#--得到phyloserq对象并提取必要数据信息
library(ggClusterNet)
library(phyloseq)
data(ps)
otu = as.data.frame(t(vegan_otu(ps)))
head(otu)## KO1 KO2 KO3 KO4 KO5 KO6 OE1 OE2 OE3 OE4 OE5 OE6 WT1 WT2
## ASV_1 1113 1968 816 1372 1062 1087 1270 1637 1368 962 1247 1017 2345 2538
## ASV_1591 12 10 1 14 3 12 15 16 4 16 31 18 17 58
## ASV_657 5 7 6 9 8 6 16 26 9 6 9 11 2 1
## ASV_28 253 303 54 439 132 182 154 263 153 257 242 178 391 450
## ASV_1717 3 6 0 1 0 4 2 6 2 0 1 2 4 0
## ASV_2407 1 2 7 0 1 7 2 2 1 2 3 8 4 1
## WT3 WT4 WT5 WT6
## ASV_1 1722 2004 1439 1558
## ASV_1591 17 34 99 11
## ASV_657 3 8 18 14
## ASV_28 235 321 449 242
## ASV_1717 1 1 0 8
## ASV_2407 2 0 1 3tax = as.data.frame((vegan_tax(ps)))
head(tax)## Kingdom Phylum Class Order
## ASV_1 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_1591 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_657 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_28 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_1717 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_2407 Bacteria Actinobacteria Actinobacteria Actinomycetales
## Family Genus Species
## ASV_1 Thermomonosporaceae Unassigned Unassigned
## ASV_1591 Thermomonosporaceae Unassigned Unassigned
## ASV_657 Thermomonosporaceae Unassigned Unassigned
## ASV_28 Thermomonosporaceae Actinocorallia Unassigned
## ASV_1717 Thermomonosporaceae Actinomadura Unassigned
## ASV_2407 Thermomonosporaceae Actinomadura Unassignedmap = sample_data(ps)
head(map)## Group Date Site Sequencing Platform Species Batch
## KO1 KO 2017/6/30 Beijing BGI HiSeq2500 Arabidopsis 1
## KO2 KO 2017/6/30 Beijing BGI HiSeq2500 Arabidopsis 1
## KO3 KO 2017/7/2 Sanya BGI HiSeq2500 Arabidopsis 1
## KO4 KO 2017/7/2 Sanya BGI HiSeq2500 Arabidopsis 1
## KO5 KO 2017/7/4 Harbin BGI HiSeq2500 Arabidopsis 1
## KO6 KO 2017/7/5 Harbin BGI HiSeq2500 Arabidopsis 1
## BarcodeSequence LinkerPrimerSequence ReversePrimer
## KO1 ACGCTCGACA AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO2 ATCAGACACG AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO3 ATATCGCGAG AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO4 CACGAGACAG AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO5 CTCGCGTGTC AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO6 TAGTATCAGC AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC#--首先构造SummarizedExperiment对象，比较简单，类似phyloseq对象
micro <- SummarizedExperiment(assays=list(counts=as.matrix(otu)),
colData=map,
rowData=tax)
# 将SummarizedExperiment对象封装成为ExperimentList
mlist <- ExperimentList()
mlist[[1]] = micro
names(mlist) = "MicrobeGenetics"# 注意必须命名，否则无法区分每个部分数据组
# 构造不同数据组之间的记录文件
gistmap <- data.frame(
primary = row.names(map),
colname = row.names(map),
stringsAsFactors = FALSE)
maplistowe <- list(MicrobeGenetics = gistmap)
sampMapowe <- listToMap(maplistowe)
# colData文件为分组文件，数据框即可，本案例只有一个微生物组数据，所以直接用map文件就可以了。
#-下面就直接构建了MultiAssayExperiment文件
mae <- MultiAssayExperiment(experiments = mlist, colData = map,
sampleMap = sampMapowe)运行Shiny app-导入数据-网页分析
微生物组数据基本统计和描述
这部分用于统计每个样本中OTU的数量，并做两种方式可视化：频率曲线，箱线 + 散点图；如果使用shiny程序的话，直接可以展示表格。 此外，可以按照微生物分类水平合并OTU数据：
p <- filter_summary_pie_box(mae,
samples_discard = c("subject_2", "subject_4"),
filter_type = "By Metadata",
sample_condition = "Group")
p更换分组，重新统计。
p <- filter_summary_bar_density(mae,
samples_discard = c("subject_2", "subject_4"),
filter_type = "By Metadata",
sample_condition = "Group")
p微生物组数据分析-微生物丰度展示-堆叠柱状图
通过tax_level选择某个分类等级，通过 sample_conditions 选择需要添加的分组标签。值得注意的是这里可以对堆叠柱状图排序的，通过order_organisms来指定，默认丰度从高到低。这里从源代码来看就是通过改变factor来实现的，所以图例第一个也是排序的这个微生物。
p <- relabu_barplot(mae,
tax_level="Family",
# order_organisms=c('Retroviridae'),
sort_by="nosort",
sample_conditions= "Group",
show_legend=TRUE)
p微生物组数据分析-微生物丰度展示-热图
p <- relabu_heatmap(mae,
tax_level="Genus",
# sort_by="conditions",
sample_conditions=c("Group"))
p微生物丰度展示-箱线图
library(tidyverse)
tax = vegan_tax(ps) %>% as.data.frame()
Gname <- table(tax$Genus) %>%names()
p <- relabu_boxplot(mae,
tax_level="Genus",
organisms=Gname[1:3],
condition="Group",
datatype="logcpm")
p微生物组数据分析-多样性-alpha多样性
这里有四个多样性指标。然后通过箱线+散点图展示。
alpha_div_boxplot(MAE = mae,
tax_level = "Genus",
condition = "Group",
alpha_metric = "shannon") 对多样性进行统计检验。这里可选的是“Wilcoxon rank sum test”, “T-test”, “Kruskal-Wallis”这三种方法。这里作者检测仅仅支持分组文件是数值的（因为作者使用了cor.test函数），不够人性化。
otu = as.data.frame(t(vegan_otu(ps)))
head(otu)## KO1 KO2 KO3 KO4 KO5 KO6 OE1 OE2 OE3 OE4 OE5 OE6 WT1 WT2
## ASV_1 1113 1968 816 1372 1062 1087 1270 1637 1368 962 1247 1017 2345 2538
## ASV_1591 12 10 1 14 3 12 15 16 4 16 31 18 17 58
## ASV_657 5 7 6 9 8 6 16 26 9 6 9 11 2 1
## ASV_28 253 303 54 439 132 182 154 263 153 257 242 178 391 450
## ASV_1717 3 6 0 1 0 4 2 6 2 0 1 2 4 0
## ASV_2407 1 2 7 0 1 7 2 2 1 2 3 8 4 1
## WT3 WT4 WT5 WT6
## ASV_1 1722 2004 1439 1558
## ASV_1591 17 34 99 11
## ASV_657 3 8 18 14
## ASV_28 235 321 449 242
## ASV_1717 1 1 0 8
## ASV_2407 2 0 1 3tax = as.data.frame((vegan_tax(ps)))
head(tax)## Kingdom Phylum Class Order
## ASV_1 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_1591 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_657 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_28 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_1717 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_2407 Bacteria Actinobacteria Actinobacteria Actinomycetales
## Family Genus Species
## ASV_1 Thermomonosporaceae Unassigned Unassigned
## ASV_1591 Thermomonosporaceae Unassigned Unassigned
## ASV_657 Thermomonosporaceae Unassigned Unassigned
## ASV_28 Thermomonosporaceae Actinocorallia Unassigned
## ASV_1717 Thermomonosporaceae Actinomadura Unassigned
## ASV_2407 Thermomonosporaceae Actinomadura Unassignedmap = sample_data(ps)
head(map)## Group Date Site Sequencing Platform Species Batch
## KO1 KO 2017/6/30 Beijing BGI HiSeq2500 Arabidopsis 1
## KO2 KO 2017/6/30 Beijing BGI HiSeq2500 Arabidopsis 1
## KO3 KO 2017/7/2 Sanya BGI HiSeq2500 Arabidopsis 1
## KO4 KO 2017/7/2 Sanya BGI HiSeq2500 Arabidopsis 1
## KO5 KO 2017/7/4 Harbin BGI HiSeq2500 Arabidopsis 1
## KO6 KO 2017/7/5 Harbin BGI HiSeq2500 Arabidopsis 1
## BarcodeSequence LinkerPrimerSequence ReversePrimer
## KO1 ACGCTCGACA AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO2 ATCAGACACG AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO3 ATATCGCGAG AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO4 CACGAGACAG AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO5 CTCGCGTGTC AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO6 TAGTATCAGC AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCCmap$Group = map$Group %>% as.numeric()
#--首先构造SummarizedExperiment对象，比较简单，类似phyloseq对象
micro <- SummarizedExperiment(assays=list(counts=as.matrix(otu)),
colData=map,
rowData=tax)
# 将SummarizedExperiment对象封装成为ExperimentList
mlist <- ExperimentList()
mlist[[1]] = micro
names(mlist) = "MicrobeGenetics"# 注意必须命名，否则无法区分每个部分数据组
# 构造不同数据组之间的记录文件
gistmap <- data.frame(
primary = row.names(map),
colname = row.names(map),
stringsAsFactors = FALSE)
maplistowe <- list(MicrobeGenetics = gistmap)
sampMapowe <- listToMap(maplistowe)
# colData文件为分组文件，数据框即可，本案例只有一个微生物组数据，所以直接用map文件就可以了。
#-下面就直接构建了MultiAssayExperiment文件
mae <- MultiAssayExperiment(experiments = mlist, colData = map,
sampleMap = sampMapowe)
do_alpha_div_test(MAE = mae,
tax_level = "Genus",
condition = "Group",
alpha_metric = "inverse_simpson",
alpha_stat = "T-test")## output
## Method Pearson's product-moment correlation
## P-value 0.71148699581376微生物组数据分析-多样性-beta多样性-聚类距离热图
diversity_beta_heatmap(MAE = mae,
tax_level = 'Genus',
input_beta_method = "bray",
input_bdhm_select_conditions = 'Group',
input_bdhm_sort_by = 'condition')其次通过组内距离和组件距离的箱线图展示
diversity_beta_boxplot(MAE = mae,
tax_level = 'Genus',
input_beta_method = "bray",
input_select_beta_condition = 'Group')再有就是统计检验,共有三种方法可以选择：PERMANOVA,Kruskal-Wallis,Wilcoxon test。 但是只有两种距离可供选择，其次就是两两比较不能实现。
diversity_beta_test(MAE = mae,
tax_level = 'Genus',
input_beta_method = "bray",
input_select_beta_condition = 'Group',
input_select_beta_stat_method = 'PERMANOVA',
input_num_permutation_permanova = 999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## vegan::adonis2(formula = dist.mat ~ condition, data = sam_table, permutations = input_num_permutation_permanova)
## Df SumOfSqs R2 F Pr(>F)
## condition 1 0.029817 0.12357 2.256 0.019 *
## Residual 16 0.211468 0.87643
## Total 17 0.241285 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1微生物组数据分析-排序 四种方法
PCA
result <- dimred_pca(mae,
tax_level="Genus",
color="Group",
shape="Group",
pcx=1,
pcy=2,
datatype="logcpm")
result$plotPCoA
result <- dimred_pcoa(mae,
tax_level="Genus",
color="Group",
shape="Group",
axx=1,
axy=2,
method="bray")
result$plotUMAP
esult <- dimred_umap(mae,
tax_level="Genus",
color="Group",
shape="Group",
cx=1,
cy=2,
n_neighbors=15,
metric="euclidean",
datatype="logcpm")
result$plott-SNE，除了二维图形展示还可以进行三维图形的展示。
result <- dimred_tsne(mae,
tax_level="Genus",
color="Group",
shape="Group",
k="3D",
initial_dims=30,
perplexity=10,
datatype="logcpm")
result$plot微生物组数据分析-差异分析
p <- differential_abundance(mae,
tax_level="Phylum",
input_da_condition=c("Group"),
min_num_filter = 2,
input_da_padj_cutoff = 0.5)
p## microbe padj pValue log2FoldChange experiment
## 1 Proteobacteria 5.66e-05 3.54e-06 1.030 1: 6/6
## 2 Planctomycetes 2.08e-04 2.60e-05 -1.350 1: 6/6
## 3 Actinobacteria 4.05e-03 9.18e-04 0.591 1: 6/6
## 4 Verrucomicrobia 4.05e-03 1.01e-03 -0.852 1: 6/6
## 5 Acidobacteria 1.43e-01 5.35e-02 -0.552 1: 6/6
## 6 Unassigned 1.43e-01 5.03e-02 0.480 1: 6/6
## 7 Armatimonadetes 1.49e-01 6.50e-02 -0.843 1: 6/6
## 8 Candidatus_Saccharibacteria 1.88e-01 9.38e-02 0.946 1: 6/6
## 9 Chloroflexi 2.89e-01 1.63e-01 0.459 1: 6/6
## 10 Thaumarchaeota 4.65e-01 2.91e-01 -1.300 1: 2/6
## 11 Firmicutes 3.40e-06 2.13e-07 1.570 1: 6/6
## 12 Proteobacteria 1.61e-02 2.01e-03 0.689 1: 6/6
## 13 Candidatus_Saccharibacteria 6.15e-02 1.48e-02 1.400 1: 6/6
## 14 Verrucomicrobia 6.15e-02 1.54e-02 -0.631 1: 6/6
## 15 Armatimonadetes 7.15e-02 2.23e-02 -1.040 1: 6/6
## 16 Unassigned 1.08e-01 4.07e-02 0.502 1: 6/6
## 17 Planctomycetes 3.17e-01 1.39e-01 -0.482 1: 6/6
## 18 Nitrospirae 4.26e-01 2.13e-01 -0.472 1: 6/6
## 19 Ignavibacteriae 4.42e-01 2.49e-01 0.988 1: 5/6
## 20 Actinobacteria 4.44e-01 2.80e-01 0.193 1: 6/6
## 21 Thaumarchaeota 4.44e-01 3.05e-01 -1.260 1: 2/6
## 22 Chlamydiae 4.52e-01 3.39e-01 -0.453 1: 6/6
## 23 Acidobacteria 4.94e-01 4.32e-01 -0.225 1: 6/6
## 24 Spirochaetes 4.94e-01 4.12e-01 -0.287 1: 6/6
## 25 Firmicutes 3.75e-07 2.34e-08 1.690 2: 12/6
## 26 Planctomycetes 4.53e-02 5.66e-03 0.872 2: 12/6
## 27 Actinobacteria 1.35e-01 2.54e-02 -0.399 2: 12/6
## 28 Chloroflexi 3.89e-01 1.03e-01 -0.536 2: 12/6
## 29 Proteobacteria 3.89e-01 1.22e-01 -0.345 2: 12/6
## 30 Acidobacteria 4.43e-01 2.49e-01 0.327 2: 12/6
## 31 Ignavibacteriae 4.43e-01 2.18e-01 1.040 2: 11/6
## 32 Nitrospirae 4.43e-01 1.93e-01 -0.483 2: 12/6
## 33 Spirochaetes 4.43e-01 2.46e-01 -0.401 2: 12/6
## control prevalence Group Size adjusted fold change Contrast
## 1 2: 6/6 66.67% 1.00 1 vs. 2
## 2 2: 6/6 66.67% 1.00 1 vs. 2
## 3 2: 6/6 66.67% 1.00 1 vs. 2
## 4 2: 6/6 66.67% 1.00 1 vs. 2
## 5 2: 6/6 66.67% 1.00 1 vs. 2
## 6 2: 6/6 66.67% 1.00 1 vs. 2
## 7 2: 6/6 66.67% 1.00 1 vs. 2
## 8 2: 6/6 66.67% 1.00 1 vs. 2
## 9 2: 6/6 66.67% 1.00 1 vs. 2
## 10 2: 5/6 38.89% 2.50 1 vs. 2
## 11 3: 6/6 66.67% 1.00 1 vs. 3
## 12 3: 6/6 66.67% 1.00 1 vs. 3
## 13 3: 6/6 66.67% 1.00 1 vs. 3
## 14 3: 6/6 66.67% 1.00 1 vs. 3
## 15 3: 6/6 66.67% 1.00 1 vs. 3
## 16 3: 6/6 66.67% 1.00 1 vs. 3
## 17 3: 6/6 66.67% 1.00 1 vs. 3
## 18 3: 6/6 66.67% 1.00 1 vs. 3
## 19 3: 6/6 61.11% 1.20 1 vs. 3
## 20 3: 6/6 66.67% 1.00 1 vs. 3
## 21 3: 5/6 38.89% 2.50 1 vs. 3
## 22 3: 6/6 66.67% 1.00 1 vs. 3
## 23 3: 6/6 66.67% 1.00 1 vs. 3
## 24 3: 6/6 66.67% 1.00 1 vs. 3
## 25 3: 6/6 100.00% 2.00 2 vs. 3
## 26 3: 6/6 100.00% 2.00 2 vs. 3
## 27 3: 6/6 100.00% 2.00 2 vs. 3
## 28 3: 6/6 100.00% 2.00 2 vs. 3
## 29 3: 6/6 100.00% 2.00 2 vs. 3
## 30 3: 6/6 100.00% 2.00 2 vs. 3
## 31 3: 6/6 94.44% 1.83 2 vs. 3
## 32 3: 6/6 100.00% 2.00 2 vs. 3
## 33 3: 6/6 100.00% 2.00 2 vs. 3微生物组数据分析-生物标记物分析
这里可选的方法有两个：“logistic regression”, “random forest”。这里去除一个分组。因为ROC只能支持两个分组。
ps <- subset_samples(ps,!Group %in% c("OE"));ps## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 2861 taxa and 12 samples ]
## sample_data() Sample Data: [ 12 samples by 10 sample variables ]
## tax_table() Taxonomy Table: [ 2861 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 2861 tips and 2860 internal nodes ]otu = as.data.frame(t(vegan_otu(ps)))
head(otu)## KO1 KO2 KO3 KO4 KO5 KO6 WT1 WT2 WT3 WT4 WT5 WT6
## ASV_1 1113 1968 816 1372 1062 1087 2345 2538 1722 2004 1439 1558
## ASV_1591 12 10 1 14 3 12 17 58 17 34 99 11
## ASV_657 5 7 6 9 8 6 2 1 3 8 18 14
## ASV_28 253 303 54 439 132 182 391 450 235 321 449 242
## ASV_1717 3 6 0 1 0 4 4 0 1 1 0 8
## ASV_2407 1 2 7 0 1 7 4 1 2 0 1 3tax = as.data.frame((vegan_tax(ps)))
head(tax)## Kingdom Phylum Class Order
## ASV_1 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_1591 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_657 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_28 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_1717 Bacteria Actinobacteria Actinobacteria Actinomycetales
## ASV_2407 Bacteria Actinobacteria Actinobacteria Actinomycetales
## Family Genus Species
## ASV_1 Thermomonosporaceae Unassigned Unassigned
## ASV_1591 Thermomonosporaceae Unassigned Unassigned
## ASV_657 Thermomonosporaceae Unassigned Unassigned
## ASV_28 Thermomonosporaceae Actinocorallia Unassigned
## ASV_1717 Thermomonosporaceae Actinomadura Unassigned
## ASV_2407 Thermomonosporaceae Actinomadura Unassignedmap = sample_data(ps)
head(map)## Group Date Site Sequencing Platform Species Batch
## KO1 KO 2017/6/30 Beijing BGI HiSeq2500 Arabidopsis 1
## KO2 KO 2017/6/30 Beijing BGI HiSeq2500 Arabidopsis 1
## KO3 KO 2017/7/2 Sanya BGI HiSeq2500 Arabidopsis 1
## KO4 KO 2017/7/2 Sanya BGI HiSeq2500 Arabidopsis 1
## KO5 KO 2017/7/4 Harbin BGI HiSeq2500 Arabidopsis 1
## KO6 KO 2017/7/5 Harbin BGI HiSeq2500 Arabidopsis 1
## BarcodeSequence LinkerPrimerSequence ReversePrimer
## KO1 ACGCTCGACA AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO2 ATCAGACACG AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO3 ATATCGCGAG AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO4 CACGAGACAG AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO5 CTCGCGTGTC AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC
## KO6 TAGTATCAGC AACMGGATTAGATACCCKG ACGTCATCCCCACCTTCC#--首先构造SummarizedExperiment对象，比较简单，类似phyloseq对象
micro <- SummarizedExperiment(assays=list(counts=as.matrix(otu)),
colData=map,
rowData=tax)
# 将SummarizedExperiment对象封装成为ExperimentList
mlist <- ExperimentList()
mlist[[1]] = micro
names(mlist) = "MicrobeGenetics"# 注意必须命名，否则无法区分每个部分数据组
# 构造不同数据组之间的记录文件
gistmap <- data.frame(
primary = row.names(map),
colname = row.names(map),
stringsAsFactors = FALSE)
maplistowe <- list(MicrobeGenetics = gistmap)
sampMapowe <- listToMap(maplistowe)
# colData文件为分组文件，数据框即可，本案例只有一个微生物组数据，所以直接用map文件就可以了。
#-下面就直接构建了MultiAssayExperiment文件
mae <- MultiAssayExperiment(experiments = mlist, colData = map,
sampleMap = sampMapowe)
p <- find_biomarker(mae,
tax_level = "Genus",
input_select_target_biomarker = c("Group"),
nfolds = 3,
nrepeats = 6,
seed = 99,
percent_top_biomarker = 0.2,
model_name = "logistic regression")
#-提取生物标记物
p$biomarker## biomarker_list
## 1 Thermogutta
## 2 Bacteriovorax
## 3 Ohtaekwangia
## 4 Methyloversatilis
## 5 Paenibacillus
## 6 Kineosporia
## 7 Chitinophaga
## 8 Lysobacter
## 9 Virgisporangium
## 10 Ammoniibacillus
## 11 Rhizobacter
## 12 Methylovorus
## 13 Gp11
## 14 Gp6
## 15 Kribbella
## 16 Armatimonadetes_gp5
## 17 Permianibacter
## 18 Chryseolinea
## 19 Gp17
## 20 Aquihabitans
## 21 Rhizobium
## 22 Nannocystis
## 23 Brevundimonas
## 24 Haliangium
## 25 Turneriella
## 26 Niastella
## 27 Streptomyces
## 28 Methyloceanibacter
## 29 Opitutus
## 30 Bacillus
## 31 Bradyrhizobium
## 32 Pelomonas
## 33 Devosia
## 34 Sphingomonas
## 35 Massilia
## 36 Nocardioides
## 37 Piscinibacter
## 38 Steroidobacter
## 39 Hyphomicrobium
## 40 Polaromonas
## 41 Aeromicrobium
## 42 Ramlibacter
## 43 Phenylobacterium#对重要变量可视化。
# importance plot
p$importance_plot
#ROC曲线准确度评估。注意ROC曲线只能对二分便量进行操作。
# ROC plot
p$roc_plot
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Chinese (Simplified)_China.936
## [2] LC_CTYPE=Chinese (Simplified)_China.936
## [3] LC_MONETARY=Chinese (Simplified)_China.936
## [4] LC_NUMERIC=C
## [5] LC_TIME=Chinese (Simplified)_China.936
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] caret_6.0-88 lattice_0.20-44
## [3] forcats_0.5.1 stringr_1.4.0
## [5] dplyr_1.0.6 purrr_0.3.4
## [7] readr_1.4.0 tidyr_1.1.3
## [9] tibble_3.1.2 ggplot2_3.3.3
## [11] tidyverse_1.3.1 phyloseq_1.36.0
## [13] ggClusterNet_0.1.0 MultiAssayExperiment_1.18.0
## [15] SummarizedExperiment_1.22.0 Biobase_2.52.0
## [17] GenomicRanges_1.44.0 GenomeInfoDb_1.28.0
## [19] IRanges_2.26.0 S4Vectors_0.30.0
## [21] BiocGenerics_0.38.0 MatrixGenerics_1.4.0
## [23] matrixStats_0.58.0 animalcules_1.9.1
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.2.1 igraph_1.2.6
## [4] plyr_1.8.6 lazyeval_0.2.2 splines_4.1.0
## [7] crosstalk_1.1.1 BiocParallel_1.26.0 digest_0.6.27
## [10] foreach_1.5.1 htmltools_0.5.1.1 fansi_0.5.0
## [13] magrittr_2.0.1 memoise_2.0.0 covr_3.5.1
## [16] cluster_2.1.2 limma_3.48.0 recipes_0.1.16
## [19] Biostrings_2.60.0 annotate_1.70.0 modelr_0.1.8
## [22] gower_0.2.2 askpass_1.1 prettyunits_1.1.1
## [25] colorspace_2.0-1 rvest_1.0.0 blob_1.2.1
## [28] haven_2.4.1 xfun_0.23 crayon_1.4.1
## [31] RCurl_1.98-1.3 jsonlite_1.7.2 genefilter_1.74.0
## [34] survival_3.2-11 iterators_1.0.13 ape_5.5
## [37] glue_1.4.2 gtable_0.3.0 ipred_0.9-11
## [40] zlibbioc_1.38.0 XVector_0.32.0 DelayedArray_0.18.0
## [43] Rhdf5lib_1.14.0 shape_1.4.6 GUniFrac_1.2
## [46] rentrez_1.2.3 scales_1.1.1 DBI_1.1.1
## [49] Rcpp_1.0.6 plotROC_2.2.1 progress_1.2.2
## [52] viridisLite_0.4.0 xtable_1.8-4 reticulate_1.20
## [55] bit_4.0.4 tsne_0.1-3 lava_1.6.9
## [58] umap_0.2.7.0 prodlim_2019.11.13 DT_0.18
## [61] glmnet_4.1-1 htmlwidgets_1.5.3 rex_1.2.0
## [64] httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2
## [67] farver_2.1.0 pkgconfig_2.0.3 XML_3.99-0.6
## [70] dbplyr_2.1.1 nnet_7.3-16 sass_0.4.0
## [73] locfit_1.5-9.4 utf8_1.2.1 labeling_0.4.2
## [76] tidyselect_1.1.1 rlang_0.4.11 reshape2_1.4.4
## [79] AnnotationDbi_1.54.0 cellranger_1.1.0 munsell_0.5.0
## [82] tools_4.1.0 cachem_1.0.5 cli_2.5.0
## [85] generics_0.1.0 RSQLite_2.2.7 ade4_1.7-16
## [88] broom_0.7.6 evaluate_0.14 biomformat_1.20.0
## [91] fastmap_1.1.0 yaml_2.2.1 fs_1.5.0
## [94] ModelMetrics_1.2.2.2 knitr_1.33 bit64_4.0.5
## [97] KEGGREST_1.32.0 nlme_3.1-152 xml2_1.3.2
## [100] rstudioapi_0.13 compiler_4.1.0 plotly_4.9.3
## [103] png_0.1-7 reprex_2.0.0 geneplotter_1.70.0
## [106] bslib_0.2.5.1 stringi_1.6.2 highr_0.9
## [109] RSpectra_0.16-0 Matrix_1.3-3 multtest_2.48.0
## [112] shinyjs_2.0.0 vegan_2.5-7 permute_0.9-5
## [115] vctrs_0.3.8 pillar_1.6.1 lifecycle_1.0.0
## [118] rhdf5filters_1.4.0 jquerylib_0.1.4 data.table_1.14.0
## [121] bitops_1.0-7 R6_2.5.0 codetools_0.2-18
## [124] MASS_7.3-54 assertthat_0.2.1 rhdf5_2.36.0
## [127] openssl_1.4.4 DESeq2_1.32.0 withr_2.4.2
## [130] GenomeInfoDbData_1.2.6 hms_1.1.0 mgcv_1.8-35
## [133] grid_4.1.0 rpart_4.1-15 timeDate_3043.102
## [136] class_7.3-19 rmarkdown_2.8 pROC_1.17.0.1
## [139] lubridate_1.7.10